Semantic consensus on model outputs for public prompts enables federated LLM fine-tuning that matches parameter-aggregation baselines with orders-of-magnitude lower communication.
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Pruning hybrid time series classifiers including the new Hydrant combination can reduce energy consumption by up to 80% while keeping accuracy loss below 5%.
A review of AI sustainability studies finds inconsistent life cycle definitions and predominant reliance on coarse CO2e proxies, with limited coverage of water, materials, and multi-impact assessments.
citing papers explorer
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Beyond Parameter Aggregation: Semantic Consensus for Federated Fine-Tuning of LLMs
Semantic consensus on model outputs for public prompts enables federated LLM fine-tuning that matches parameter-aggregation baselines with orders-of-magnitude lower communication.
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Pruning Extensions and Efficiency Trade-Offs for Sustainable Time Series Classification
Pruning hybrid time series classifiers including the new Hydrant combination can reduce energy consumption by up to 80% while keeping accuracy loss below 5%.
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From Cradle to Cloud: A Life Cycle Review of AI's Environmental Footprint
A review of AI sustainability studies finds inconsistent life cycle definitions and predominant reliance on coarse CO2e proxies, with limited coverage of water, materials, and multi-impact assessments.